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Study finds finite-width effects shift the edge of chaos in deep neural networks

This story is from 2026-11-06. It is preserved in the archive; the latest stories are on the live feed.

A new arXiv paper analyzes how finite network width shifts the edge of chaos, the critical line of weight and bias variances where deep neural networks train best at initialization.

Read the full story at arXiv stat.ML ↗

Timeline · 1 report

  1. 2026-11-06 05:00 · arXiv stat.ML
    Finite-width shift of the edge of chaos in deep neural networks

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